code: def logs():
      return [
          {
              "train_log": {
                  "xgb_classification_learner": {
                      "features": ["x1", "x2", "x4", "x5", "x3", "x6"],
                      "target": "target",
                      "prediction_column": "prediction",
                      "package": "xgboost",
                      "package_version": "0.6",
                      "parameters": {
                          "objective": "binary:logistic",
                          "max_depth": 3,
                          "min_child_weight": 0,
                          "lambda": 0,
                          "eta": 1,
                      },
                      "feature_importance": {
                          "x1": 8,
                          "x5": 2,
                          "x3": 3,
                          "x6": 1,
                          "x2": 1,
                      },
                      "training_samples": 8,
                      "running_time": "0.019 s",
                  }
              },
              "validator_log": [
                  {
                      "fold_num": 0,
                      "eval_results": [
                          {"roc_auc_evaluator__target": 0.8}
                      ],
                      "split_log": {"train_size": 8, "test_size": 8},
                  },
                  {
                      "fold_num": 1,
                      "eval_results": [
                          {"roc_auc_evaluator__target": 0.8}
                      ],
                      "split_log": {"train_size": 8, "test_size": 8},
                  },
              ],
              "used_subsets": ["first", "second"],
          },
          {
              "train_log": {
                  "xgb_classification_learner": {
                      "features": ["x1", "x2", "x4", "x5", "x3", "x6"],
                      "target": "target",
                      "prediction_column": "prediction",
                      "package": "xgboost",
                      "package_version": "0.6",
                      "parameters": {
                          "objective": "binary:logistic",
                          "max_depth": 3,
                          "min_child_weight": 0,
                          "lambda": 0,
                          "eta": 1,
                      },
                      "feature_importance": {
                          "x1": 8,
                          "x5": 2,
                          "x3": 3,
                          "x6": 1,
                          "x2": 1,
                      },
                      "training_samples": 8,
                      "running_time": "0.019 s",
                  }
              },
              "validator_log": [
                  {
                      "fold_num": 0,
                      "eval_results": [
                          {"roc_auc_evaluator__target": 0.6}
                      ],
                      "split_log": {"train_size": 8, "test_size": 8},
                  },
                  {
                      "fold_num": 1,
                      "eval_results": [
                          {"roc_auc_evaluator__target": 0.6}
                      ],
                      "split_log": {"train_size": 8, "test_size": 8},
                  },
              ],
              "used_subsets": ["first", "third"],
          },
      ]
  
file_path: fklearn/tests/tuning/test_utils.py
imports: import pandas
  import pytest
  from fklearn.training.classification import logistic_classification_learner
  from fklearn.metrics.pd_extractors import evaluator_extractor
  from fklearn.tuning.utils import get_avg_metric_from_extractor, get_best_performing_log, get_used_features, gen_key_avgs_from_dicts, gen_key_avgs_from_logs, order_feature_importance_avg_from_logs, gen_key_avgs_from_iteration, gen_dict_extract
label_desc: Use image node_id 1 for calling a global function with example usage: logs() and returns: list